An approach to human iris recognition using quantitative analysis of image features and machine learning
Computer Vision and Pattern Recognition
2020-09-15 v1 Machine Learning
Image and Video Processing
Abstract
The Iris pattern is a unique biological feature for each individual, making it a valuable and powerful tool for human identification. In this paper, an efficient framework for iris recognition is proposed in four steps. (1) Iris segmentation (using a relative total variation combined with Coarse Iris Localization), (2) feature extraction (using Shape&density, FFT, GLCM, GLDM, and Wavelet), (3) feature reduction (employing Kernel-PCA) and (4) classification (applying multi-layer neural network) to classify 2000 iris images of CASIA-Iris-Interval dataset obtained from 200 volunteers. The results confirm that the proposed scheme can provide a reliable prediction with an accuracy of up to 99.64%.
Cite
@article{arxiv.2009.05880,
title = {An approach to human iris recognition using quantitative analysis of image features and machine learning},
author = {Abolfazl Zargari Khuzani and Najmeh Mashhadi and Morteza Heidari and Donya Khaledyan},
journal= {arXiv preprint arXiv:2009.05880},
year = {2020}
}